How do we make decisions as consumers? What do we pay attention to, and how do our initial responses predict our final choices? To what extent are these processes unconscious and cannot be reflected in overt reports? This course will provide you with an introduction to some of the most basic methods in the emerging fields of consumer neuroscience and neuromarketing. You will learn about the methods employed and what they mean. You will learn about the basic brain mechanisms in consumer choice, and how to stay updated on these topics. The course will give an overview of the current and future uses of neuroscience in business.

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Attention & Consciousness

In the second module of this course we are turning to the functions of the brain, and we will first focus on attention and consciousness. This module will focus on some key concepts in attention and consciousness.

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Thomas Zoëga Ramsøy

PhD in Neurobiology, Certified Neuropsychologist & Assistant Professor in Marketing & NeuroscienceHead of the Center for Decision Neuroscience, Department of Marketing at Copenhagen Business School, and Head of Danish Research Centre for Magnetic Resonance at Copenhagen University Hospital Hvidovre

Okay, so this will be a hands-on demo of one of

the computational neuroscience tools that have appeared recently.

Computational neuroscience is a way to use computational methods,

meaning making models, making tools.

Based on neuroscience, that can allow us to, better understand but

also better predict what people are going to do.

And, the current tool here is NeuroVision.

NeuroVision is a tool that allows researchers to upload videos and

images and have that analyzed on the fly.

Neurovision, as we have seen in some of the other talks here, is a tool that,

predicts a certain kind of attention, the bottom-up attention perspective.

So what people are automatically drawn to, things like contrast and

density and color composition.

And movement are all things that draw people's attention to them.

And the thing that we will talk about here is to show you some of

the different examples, how we can use it, and how we can interpret the results, and

how we can improve the results.

As you can see the interface here it's an online dashboard.

And that analyzes the images that you will be uploading.

For the purpose here, we have prepared some images already.

Just have to find them here.

Let's see here.

So, what we can do is, all right, first of all we can take all the images here,

but let's start with a virtual shelf for example.

We can drag and drop it over to the folder here.

It will upload, leave me a image.

And for now we'll focus on images.

It analyzes that image, and

in a few seconds the analysis of the image is ready.

Now what it has done is to analyze the image based on its properties, such as,

as we mentioned, density, contrast, color composition.

There's no movement here, but in movies there will be movement as well.

Okay, so we can have a look at the results.

First of all, let's have a look at the basic image.

I think we should this is a retail image, so just for benchmarking the results.

So what we can first of all can see here is that you can see

the store shelf here is pretty busy.

You have some things up here.

You have these products, and these are all beer.

Just grab from the web.

This is just an image I have taken from the web.

What witnessing here is that the visual complexity is pretty high, it's 89.2%.

This is an indication that there's a lot of things going on in the image, such as,

basically showing that there's no, or very few, blank surfaces here.

There's a lot of information, little crowding, so to speak.

And that means that the likelihood that any one item will pick up on

people's attention is going to be very low.

So the, the competition for automatic attention is very high, so to speak.

Let's have a look at the heat map.

Okay, so what we're going to see here at the heat map,

the hottest region here is the.

Yeah, for Xs for example, what you can see here is that there seems

to be something with the contrast between there opposition and the shelf itself.

That seems to,

tra, kind of trade this huge kind of huge contrast that people are likely to spot

something down here with some other yellow and, and black sh products as well.

You can also see that the middle ones,

the green ones here is the call space, let's assume is, is not very high.

You can also see the bottom here.

Just, too many people may be surprising, but if you look at shelf, it's very dark.

One of the things that the eyes are attracted very much to is dark regions.

And what we can see is that also that, people are beginning to discuss okay what,

how should we design the shelves themselves.

So, here you are it is the results is,

is basically showing that there are certain products that due to

the internal properties their, their density their contrast.

Their their brightness and the image for example also the color composition and

relative to their neighbors of stealing more attention, automatic attention.

They're more likely to be noticed than other things,

a different way of looking at that is by using what we call the fog map.

The fog map is just an inversion of the heat map,

to illustrate what things are going to be more likely in popping out.

So you can see there the f,

have fogged out the things that are less likely to be noticed here.

The saliency score here is an indication of the complexity of the heat map.

So, if you have a lot of complexity, just a lot heat dots all,

scattered everywhere, the saliency spot will be very high.

What you can see here is that there's still few,

relatively few regions showing this, this effect.

When you look at the arid distribution of the high, which is the hottest colors.

The dark, dark, purpleness here, the low which is the red to orange,

the static area, which is the yellow to, especially to the sign here.

And, of course, then the neutral areas that don't attract any attention at all,

except the high region 80%, almost 20% of the images; pretty high.

So, this indicates that there are several other things that

are going to attract people's attention automatically.

There will be a lot of competition for people's attention.

Now, let's turn to another analysis.

And what we can look at here.

We can look at, for example, an image like this, a store like this.

You can almost, just looking at this image here,

what do you believe will be the most, the most visually appealing thing here.

My guess would be these down here, would be pretty appealing.

I would guess the dress over here would be pretty appealing as well.

And there might be some things up here that,

due to contrast with the background here.

Just stand out, they pop out, and probably this dress over here as well.

We'll have to see.

So we upload image, we run the analysis.

The analysis only takes a few seconds and because the computer is

let's call it cloud bases system is just looking at the image.

Analyze the image and looking at what kind of.

Image that we're looking at.

What, what kind of properties does it have?

First of all, you can notice that the visual complexity is much lower here.

It's 28.9%.

Which shows us that there are still a lot of things going on,

but not nearly as much as is in the previously example.

We also see the saliency.

Keep that and the high distribution area here is very low.

It's 6% and, and 7.25% which is pretty low.

It will indicate that it's not meant to be a heat matter scattered all over

the place, but it's going to focus on a few things.

Okay, so I was wrong.

One thing I was right about this here.

I was right about this one up here, I didn't see this one over here and

I was wrong about this one over here.

So it's a,

this basically say that there are things like these things that are popping out

that people are automatically more likely to pay attention to than other things.

So this all depends on if that is what you want.

If you don't want people to have that, and you want people to look more towards this.

You have to probably do something about, about taking this away,

put it away in a different way, putting it on the side here.

So it's not stealing too much attention.

The whole dictum of less is more.

Is really important when it comes to NeuroVision.

And, and bottom-up attention or

visual saliency is that if you really want to have some things in the foreground,

you need to fit some other things in the background.

And the foreground really illustrate that.

It's probably not showing this too well but

you can see that this thing over here is going to be intensive.

This one is, is stealing a lot of attention and,

of course, the dress over here.

Now, if you want to a new, one of the things that we can do

[COUGH] is to take an, look at packages for example.

Just from these, from net so which one of those are more likely to.

I draw attention.

I guess that would be pretty equal but I won't be surprised there as well.

And we can also at the same time we can take [COUGH] the packaging from Coke we'll

do that in a second.

Let's see here just just click the two of them right away.

[NOISE]

And you can upload you can batch upload something like 15 images at a time.

A video of like 30 seconds will take about seven minutes to analyze.

Let's look at the coke here first.

You can see.

We know that, high contrast images,

are going to be more noticed, than other ones.

So, my assumption here is that the black one you can almost see this instantly,

this black one is going to just steal a lot of attention.

It's probably going to, to be almost like the winner takes all.

This over here.

The question is how much will the, the information recycle.

Be grabbing any attention.

Let's have another look at that.

Not very much it seems.

So of these, it seems that this one takes a lot of attention.

The Coke bottle also does that pretty decently.

We're not going to look at automatically at the Coca-Cola but,

you know our response to Coca-Cola is so unique.

That and so strong that it's going to be and emotional response to that anyway.

So Neurovision doesn't pre, predict emotional responses only the likelihood of

people are going to notice things in an image.

You could take the other video here.

Starting the other picture here.

So the question here is.

So this is a product.

Can you see how the, the visual complexity is really high.

It shows us there's a lot of things going on in these packages.

There's a lot of but I will also say that the images themselves,

are pretty, they're not crystal clear.

That one impacts on,

on the results as well, and so the high distribution area here is really large.

So it, so at two percent it will only be a few heated areas I guess.

Yes, indeed. So

you can see here this only makes up two percentage, so

this is almost like the winner takes all.

Now you can see that this.

Product over here will out compete the other ones if they are put on the serve,

same shelf.

So the likelihood that people are going to look at this first is very high.

This doesn't take away that the likelihood that they will the other ones will,

will happen, but it's going to be a secondary process.

Again, let's go look up here.

See, this is, our objects will be the first thing that they will look at.

And then finally let's see.

How bout taking.

An image from today.

I'm, I'm in New York City today and

I what better way of giving an example than taking a snapshot from Times Square?

So let's see.

The results from minimizing the Times Square image shows us.

So this is an outdoor image.

The reason we're categorizing the images is because that we are making

different benchmarks, different normative data materials for different scenarios.

So, this Neurovision can be used for

testing packages as if you're holding it in your hand.

You can be testing shot packages on the shuttles.

You can be testing ads and brand positioning.

And product positioning can be tested like this outdoors to look at

which signs are going to be most appealing.

My guess is it's going to be pretty obvious.

This one I guess, but I might be entirely wrong.

Although I'm an expert in that doesn't mean that I'm very good at.

Predicting always what's going to be most salient.

Initial complexity is 27%, which is moderate.

It shows that there is a lot of information here.

As we can see those, some, some nice dark right there,

blue skies here, so that's not going to steal a lot of attention over here.

But we can guess that.

If you just look up here,

the resolution actually is pretty high the bus from here, so let's see.

Yes, so the Coke image here is stealing, by far, the most image attention.

So the reason we can look at that.

The reason that happened is that, not only do we have the Coke,

it's kind of the red on black.

But it also has this lines, this kind of disruptions and all this kind of angles.

And, and, one thing that, we know that the,

the, the eyes are automatically paying attention to is angles.

And, and, you know, changes in, in contrast, and changes in color and

position, and the, this Coke ad is doing a very good job of doing that.

You see, it steals a lot of attention from other things.

The likelihood that people are going to see those other things is lower,

at least from automatic perspective.

But if they spend a bit more time in looking around they will see

that imagery as well, but it Coke.

It's by far the most dominant one.

It's also, oh, probably bit likely that they will see this sign up here as well.

I don't even know why it says your, but maybe it's a shifting thing.

And the only thing, of course, with, with Times Square is that there's a lot of

movement here, and this has not been factored into it, this analysis.

If you do the video analysis I'm sure we will see some other changes as well.

So there you have it.

This has been a demo of NeurVision, a computational neuroscience tool.